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https://hdl.handle.net/2142/85416
Description
Title
Protein Structure Analysis and Prediction
Author(s)
Hunter, Cornelius George
Issue Date
2001
Doctoral Committee Chair(s)
Subramaniam, Shankar
Department of Study
Biophysics and Computational Biology
Discipline
Biophysics and Computational Biology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Molecular
Language
eng
Abstract
The protein structure prediction problem consists of three separate subproblems: modeling the protein structure, modeling the protein energetics, and the problem of searching for low-energy structures in high-dimensional space in the presence of many local minima. This study presents a new approach to protein structure prediction based on innovative methods in all three subproblems. The protein structure is modeled using natural coordinates, with less than one degree of freedom per residue. The protein energetics are modeled using two-dimensional statistical potentials and a linearized Hamiltonian. The two-dimensional statistical potentials are modeled as a function of the inter atomic distance and the local chain compression. The linearized Hamiltonian accounts for the fact that there is no one-to-one mapping between protein energy mechanisms and statistical potentials. The search for low-energy structures is done using a phenomenological structure model consisting of local structure fragments and tertiary contacts. The search space is reduced by searching along the finite set of possible tertiary contacts. The initial implementation of this approach is producing promising results. The results of this study also suggest a new method for structural alignment using natural coordinates, and new evidence for long-range sequence patterns.
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